
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_						)						->						str:
					'''simple docstring'''


					assert isinstance(UpperCamelCase_      ,      UpperCamelCase_						)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('keep_in_memory'      ,      [False, True]						)
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						Optional[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__						=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader(UpperCamelCase_      ,      cache_dir=UpperCamelCase_      ,      keep_in_memory=UpperCamelCase_						).read()
					_check_parquet_dataset(UpperCamelCase_      ,      UpperCamelCase_						)






@pytest.mark.parametrize(
    'features'      ,      [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,      )
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__						=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					SCREAMING_SNAKE_CASE__						=					features.copy() if features else default_expected_features
					SCREAMING_SNAKE_CASE__						=					(
					    Features({feature: Value(UpperCamelCase_						) for feature, dtype in features.items()}						) if features is not None else None
					)
					SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader(UpperCamelCase_      ,      features=UpperCamelCase_      ,      cache_dir=UpperCamelCase_						).read()
					_check_parquet_dataset(UpperCamelCase_      ,      UpperCamelCase_						)






@pytest.mark.parametrize('split'      ,      [None, NamedSplit('train'						), 'train', 'test']						)
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__						=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader(UpperCamelCase_      ,      cache_dir=UpperCamelCase_      ,      split=UpperCamelCase_						).read()
					_check_parquet_dataset(UpperCamelCase_      ,      UpperCamelCase_						)
					assert dataset.split == split if split else "train"






@pytest.mark.parametrize('path_type'      ,      [str, list]						)
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						Dict:
					'''simple docstring'''


					if issubclass(UpperCamelCase_      ,      UpperCamelCase_						):
										SCREAMING_SNAKE_CASE__						=					parquet_path
					elif issubclass(UpperCamelCase_      ,      UpperCamelCase_						):
										SCREAMING_SNAKE_CASE__						=					[parquet_path]
					SCREAMING_SNAKE_CASE__						=					tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__						=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader(UpperCamelCase_      ,      cache_dir=UpperCamelCase_						).read()
					_check_parquet_dataset(UpperCamelCase_      ,      UpperCamelCase_						)






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_=("train",)						)						->						Any:
					'''simple docstring'''


					assert isinstance(UpperCamelCase_      ,      UpperCamelCase_						)
					for split in splits:
										SCREAMING_SNAKE_CASE__						=					dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 3
										assert dataset.column_names == ["col_1", "col_2", "col_3"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('keep_in_memory'      ,      [False, True]						)
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__						=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader(
										    {'train': parquet_path}      ,      cache_dir=UpperCamelCase_      ,      keep_in_memory=UpperCamelCase_						).read()
					_check_parquet_datasetdict(UpperCamelCase_      ,      UpperCamelCase_						)






@pytest.mark.parametrize(
    'features'      ,      [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,      )
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__						=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					SCREAMING_SNAKE_CASE__						=					features.copy() if features else default_expected_features
					SCREAMING_SNAKE_CASE__						=					(
					    Features({feature: Value(UpperCamelCase_						) for feature, dtype in features.items()}						) if features is not None else None
					)
					SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader({'train': parquet_path}      ,      features=UpperCamelCase_      ,      cache_dir=UpperCamelCase_						).read()
					_check_parquet_datasetdict(UpperCamelCase_      ,      UpperCamelCase_						)






@pytest.mark.parametrize('split'      ,      [None, NamedSplit('train'						), 'train', 'test']						)
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						Optional[int]:
					'''simple docstring'''


					if split:
										SCREAMING_SNAKE_CASE__						=					{split: parquet_path}
					else:
										SCREAMING_SNAKE_CASE__						=					'train'
										SCREAMING_SNAKE_CASE__						=					{'train': parquet_path, 'test': parquet_path}
					SCREAMING_SNAKE_CASE__						=					tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__						=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader(UpperCamelCase_      ,      cache_dir=UpperCamelCase_						).read()
					_check_parquet_datasetdict(UpperCamelCase_      ,      UpperCamelCase_      ,      splits=list(path.keys()						)						)
					assert all(dataset[split].split == split for split in path.keys()						)






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_						)						->						Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					ParquetDatasetWriter(UpperCamelCase_      ,      tmp_path / 'foo.parquet'						)
					assert writer.write() > 0
					SCREAMING_SNAKE_CASE__						=					pq.ParquetFile(tmp_path / 'foo.parquet'						)
					SCREAMING_SNAKE_CASE__						=					pf.read()
					assert dataset.data.table == output_table






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_						)						->						List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					str(shared_datadir / 'test_image_rgb.jpg'						)
					SCREAMING_SNAKE_CASE__						=					{'image': [image_path]}
					SCREAMING_SNAKE_CASE__						=					Features({'image': Image()}						)
					SCREAMING_SNAKE_CASE__						=					Dataset.from_dict(UpperCamelCase_      ,      features=UpperCamelCase_						)
					SCREAMING_SNAKE_CASE__						=					ParquetDatasetWriter(UpperCamelCase_      ,      tmp_path / 'foo.parquet'						)
					assert writer.write() > 0

					SCREAMING_SNAKE_CASE__						=					Dataset.from_parquet(str(tmp_path / 'foo.parquet'						)						)
					assert dataset.features == reloaded_dataset.features

					SCREAMING_SNAKE_CASE__						=					ParquetDatasetReader(str(tmp_path / 'foo.parquet'						)      ,      streaming=UpperCamelCase_						).read()
					assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    'feature, expected'      ,      [
        (Features({'foo': Value('int32'						)}						), None),
        (Features({'image': Image(), 'foo': Value('int32'						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]      ,      )
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_						)						->						Optional[int]:
					'''simple docstring'''


					assert get_writer_batch_size(UpperCamelCase_						) == expected











'''simple docstring'''







import argparse
import copy







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   with open(UpperCAmelCase						) as f:
      for line in f:
         if line.split()[0] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[1], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[0]].append(
                [line.split()[1], line.split()[2]]						)
         if line.split()[1] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[0], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[1]].append(
                [line.split()[0], line.split()[2]]						)

   return dict_of_neighbours







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      A__       =     f.read(1						)
   A__       =     start_node

   A__       =     []

   A__       =     start_node

   A__       =     0
   while visiting not in first_solution:
      A__       =     10000
      for k in dict_of_neighbours[visiting]:
         if int(k[1]						) < int(UpperCAmelCase						) and k[0] not in first_solution:
            A__       =     k[1]
            A__       =     k[0]

      first_solution.append(UpperCAmelCase						)
      A__       =     distance_of_first_solution + int(UpperCAmelCase						)
      A__       =     best_node

   first_solution.append(UpperCAmelCase						)

   A__       =     0
   for k in dict_of_neighbours[first_solution[-2]]:
      if k[0] == start_node:
         break
      position += 1

   A__       =     (
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 10000
   )
   return first_solution, distance_of_first_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for n in solution[1:-1]:
      A__       =     solution.index(UpperCAmelCase						)
      for kn in solution[1:-1]:
         A__       =     solution.index(UpperCAmelCase						)
         if n == kn:
            continue

         A__       =     copy.deepcopy(UpperCAmelCase						)
         A__       =     kn
         A__       =     n

         A__       =     0

         for k in _tmp[:-1]:
            A__       =     _tmp[_tmp.index(UpperCAmelCase						) + 1]
            for i in dict_of_neighbours[k]:
               if i[0] == next_node:
                  A__       =     distance + int(i[1]						)
         _tmp.append(UpperCAmelCase						)

         if _tmp not in neighborhood_of_solution:
            neighborhood_of_solution.append(_tmp						)

   A__       =     len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda UpperCAmelCase						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     1
   A__       =     first_solution
   A__       =     []
   A__       =     distance_of_first_solution
   A__       =     solution

   while count <= iters:
      A__       =     find_neighborhood(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     0
      A__       =     neighborhood[index_of_best_solution]
      A__       =     len(UpperCAmelCase						) - 1

      A__       =     False
      while not found:
         A__       =     0
         while i < len(UpperCAmelCase						):
            if best_solution[i] != solution[i]:
               A__       =     best_solution[i]
               A__       =     solution[i]
               break
            A__       =     i + 1

         if [first_exchange_node, second_exchange_node] not in tabu_list and [
             second_exchange_node,
             first_exchange_node,
         ] not in tabu_list:
            tabu_list.append([first_exchange_node, second_exchange_node]						)
            A__       =     True
            A__       =     best_solution[:-1]
            A__       =     neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               A__       =     cost
               A__       =     solution
         else:
            A__       =     index_of_best_solution + 1
            A__       =     neighborhood[index_of_best_solution]

      if len(UpperCAmelCase						) >= size:
         tabu_list.pop(0						)

      A__       =     count + 1

   return best_solution_ever, best_cost







def 						_A      ( UpperCAmelCase=None						):






   '''simple docstring'''







   A__       =     generate_neighbours(args.File						)

   A__  ,			A__       =     generate_first_solution(
       args.File      ,UpperCAmelCase						)

   A__  ,			A__       =     tabu_search(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,args.Iterations      ,args.Size      ,)

   print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''Tabu Search''')
    parser.add_argument(
        '''-f''',
        '''--File''',
        type=str,
        help='''Path to the file containing the data''',
        required=True,
    )
    parser.add_argument(
        '''-i''',
        '''--Iterations''',
        type=int,
        help='''How many iterations the algorithm should perform''',
        required=True,
    )
    parser.add_argument(
        '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


